Browsing by Subject "Coded aperture"

Computer simulation and modeling is a powerful tool for the analysis of physical systems; in this work we consider the use of computer modeling and optimization in improving the focusing properties of a variety of charged particle optics systems. The combined use of several software packages and custom computer code allows for modeling electrostatic and magnetostatic fields and the trajectory of particles through them. Several applications of this functionality are shown. The pieces of code which are shown are the starting point of an integrated charged particle simulation and optimization tool with focus on optimization. The applications shown are mass spectrographs and electron energy spectrographs. Simulation allowed additional information about the systems in question to be determined.In this work, coded apertures are shown to be compatible with sector instruments, though architectural challenges exist. Next, simulation allowed for the discovery of a new class of mass spectrograph which addresses these challenges and is compatible with computational sensing, allowing for both high resolution and high sensitivity, with a 1.8x improvement in spot size. Finally, a portion of this new spectrograph was adapted for use as an electron energy spectrograph, with a resolution 9.1x and energy bandwidth 2.1x that of traditional instruments.

Mass spectrometry is widely considered to be the gold standard of elemental analysis techniques due to its ability to resolve atomic and molecular and biological species. Expanding the application space of mass spectrometry often requires the need for portable or hand-held systems for use in field work or harsh environments. While only requiring “sufficient” mass resolution to meet the needs of their application space, these miniaturized systems suffer from poor signal to background ratio which limits their sensitivity as well as their usefulness in field applications.

Spatial aperture coding techniques have been used in optical spectroscopy to achieve large increases in signal intensity without compromising system resolution. In this work similar computational methods are used in the application of these techniques to the field of magnetic sector mass spectrometry. Gains in signal intensity of 10x and 4x were achieved for 1D and 2D coding techniques (respectively) using a simple 90 degree magnetic sector test setup. Initial compatibility with a higher mass resolution double focusing Mattauch-Herzog mass spectrograph is demonstrated experimentally and with high fidelity particle tracing simulations. A novel electric sector lens system was designed to stigmate high order coded aperture patterned beam which shows simulated gains in signal intensity of 50x are achievable using these techniques.

Coherent scatter imaging techniques have experienced a renaissance in the past two decades from an evolution of detector technology and computational imaging techniques. X-ray diffraction requires a precise knowledge of object location and is time consuming; transforming diffractometry into a practical imaging technique involves spatially resolving the sample in 3-dimensions and speeding up the measurement process. The introduction of a coded aperture in a conventional X-ray diffraction system provides 3D localization of the scatterer as well as drastic reductions in the acquisition time due to the ability to perform multiplexed measurements. This theses document contains two strategies involving coded apertures to address the aforementioned challenges of X-ray coherent scatter measurements.

The first technique places the coded aperture between source and object to structure the incident illumination. A single pixel detector captures temporally modulated coherent scatter data from an object as it travels through the illumination. From these measurements, 2D spatial and 1D spectral information is recovered at each point within a planar slice of an object. Compared to previous techniques, this approach is able to reduce the overall scan time of objects by 1-2 orders of magnitude.

The second measurement technique demonstrates snapshot coherent scatter tomography. A planar slice of an object is illuminated by a fan beam and the scatter data is modulated by a coded aperture between object and detector. The spatially modulated data is captured with a linear array of energy sensitive detectors, and the recovered data shows that the system can image objects that are 13 mm in range and 2 mm in cross range with a fractional momentum transfer resolution of 15\%. The technique also allows a 100x speedup when compared to pencil beam systems using the same components.

Continuing with the theme of snapshot tomography with energy sensitive detectors, I study the impact of detectors properties such as detection area, choice of energies and energy resolution for pencil and fan beam coded aperture coherent scatter systems. I simulate various detector geometries and determine that energy resolution has the largest impact for pencil beam geometries while detector area has the largest impact for fan beam geometries. These results can be used to build detectors which can potentially help implement pencil and/or fan beam coded aperture coherent scatter systems in applications involving medicine and security.

This work focuses on the construction and application of coded apertures to compressive X-ray tomography. Coded apertures can be made in a number of ways, each method having an impact on system background and signal contrast. Methods of constructing coded apertures for structuring X-ray illumination and scatter are compared and analyzed. Apertures can create structured X-ray bundles that investigate specific sets of object voxels. The tailored bundles of rays form a code (or pattern) and are later estimated through computational inversion. Structured illumination can be used to subsample object voxels and make inversion feasible for low dose computed tomography (CT) systems, or it can be used to reduce background in limited angle CT systems.

On the detection side, coded apertures modulate X-ray scatter signals to determine the position and radiance of scatter points. By forming object dependent projections in measurement space, coded apertures multiplex modulated scatter signals onto a detector. The multiplexed signals can be inverted with knowledge of the code pattern and system geometry. This work shows two systems capable of determining object position and type in a 2D plane, by illuminating objects with an X-ray `fan beam,' using coded apertures and compressive measurements. Scatter tomography can help identify materials in security and medicine that may be ambiguous with transmission tomography alone.

X-ray imaging technology has been used for a multitude of medical applications over the years. The typically measured X-ray transmission data, which records shape and density information by measuring the differences in X-ray attenuation throughout a material, have been used in the imaging modalities of radiography and computed tomography (CT), but there are cases where this information alone is not enough for diagnosis. In contrast, X-ray diffraction (XRD) is another X-ray measurement modality, one that typically does not produce spatially resolved 2D/3D images, but instead investigates small spatial spots for assessing material properties/molecular structures based on scattered X-rays. While XRD measurements of human breast tissue have previously suggested differences between signatures of cancerous and benign tissues, the typical diffraction system architectures do not support fast, large field of view imaging that is necessary for medical applications.In this work, an XRD imaging system was developed that can scan a 15x15 cm2 field of view in minutes with an XRD spatial resolution of 1.4 mm2 and momentum transfer (q) resolution of 0.02 Å-1. An X-ray fan beam was used to collect a 15 cm line of XRD measurements in a single snapshot, while a coded aperture is placed between imaged objects and detector, enabling XRD spectra for individual pixels along the fan beam extent to be recovered from the multiplexed measurement. Simulations were used to identify a suitable geometry for the system, while newly designed phantoms and test objects were used to evaluate the resolution/measurement quality. Upon finishing the design, construction, and characterization of the imaging system, studies on cancerous and benign tissue simulant phantoms were conducted to develop and identify top performing machine learning classification algorithms in a well-controlled study. With a shallow neural network (SNN) developed that achieved ≈99% accuracy on XRD image data, studies progressed to real human tissues. With these developments achieved, the final study was conducted where 22 human breast lumpectomy specimens were scanned and the SNN algorithm was modified for identification of human breast cancer. For 15 primary lumpectomy cases used for training and testing, an accuracy of 99.7% was achieved, with an ROC curve AUC of 0.953 and precision-recall curve AUC of 0.771. On the remaining 7 corner/rare cases present that were held out from initial training/testing (as an external dataset), an accuracy of 99.3% was achieved by the SNN, suggesting high performance along with a need for further representation of rare tissue cases in the training process to improve classifier generalization to new lumpectomy cases. This work demonstrates that fast, large field of view XRD imaging of thin samples on a millimeter spatial scale can be achieved using coded apertures. Further, the work shows that machine learning algorithms can complement this imaging modality by making great use of the multitude of input features available when each image pixel contains a full spectrum of XRD intensity vs angle values, allowing for algorithms to differentiate between cancerous and healthy tissue with higher accuracy (99.7%) compared to simple classification approaches (97.3%). Due to this promising potential, future work should seek to further the technology, by improving the spatial/spectral resolution, scan speed, and adding depth resolution, while applying the technology to useful medical tasks including (but not limited to) intraoperative surgical margin assessment, in-vivo imaging for biopsy vetting, and improved radiation therapy tumor localization.

Aperture coding has been utilized in mass spectrometry to enable miniaturization of the instrument while maintaining resolution and throughput. In miniature cycloidal mass spectrometry, implementing aperture coding is difficult since uniform magnetic and electric fields are required in the sector region. This work shows through finite element simulation that the compatibility between a cycloidal mass analyzer and aperture coding has been improved by the development of a novel electric sector. COMSOL 5.2a was utilized as the primary finite element analysis tool, and MATLAB R2016b was used to generate most figures.

This study intends to validate the sensitivity and specificity of coded aperture coherent scatter spectral imaging (CACSSI) by comparison to clinical histological preparation and pathologic analysis methods currently used for the differentiation of normal and neoplastic breast tissues. A composite overlay of the CACSSI rendered image and pathologist interpreted, stained sections validate the ability of coherent scatter imaging to differentiate cancerous tissues from normal, healthy breast structures ex-vivo. Via comparison to the pathologist annotated slides, the CACSSI system may be further optimized to maximized sensitivity and specificity for differentiation of breast carcinomas.

Breast cancer surgical treatment options prove effective at treating breast cancer and reducing breast cancer death rates, prompting women to elect to surgically excise the tumor via a lumpectomy procedure. Despite women choosing lumpectomy over a mastectomy in 60% of cases, and despite the general effectiveness of the lumpectomy procedures, patient recall rates due to missed cancerous tissue are unfavorably high and variable at approximately 25% nationally. In addition, drawn-out processing times due to pathology assessment contribute to sub-optimal patient care and overly onerous costs and workload for hospitals. Therefore, it is the focus of this work to develop, evaluate, and refine a novel imaging modality to aid pathologists and pathologists’ assistants in assessing breast cancer via a more quantified means that would eventually lower the recall rates in breast cancer surgery.

Through previous work, we established a Coded Aperture Coherent Scatter Spectral Imaging (CACSSI) system, characterized several facets of the imaging setup, and evaluated its utility in breast cancer applications. Using Monte Carlo simulations, anthropomorphic breast phantoms, and human breast tissue specimens, we previously validated CACSSI’s utility in differentiating breast tissue types in a clinically relevant manner, which makes the system a promising candidate to act as a supplementary tool to implement in the pathology workflow. This work continues the previous research by applying and implementing the tissue classification ability within a short, clinically feasible timeframe (5-30 minutes) and demonstrating utility in a broader population of 12 patient-derived lumpectomy specimens. The work presented herein is broken into three subprojects: (1) Assessing various characterizations of the system (i.e. the background signal effects, the detector temperature-dependent response, the precision in consecutive scans, and the effect of formalin-fixation) to demonstrate its feasibility for the cancer detection/classification tasks; (2) Evaluating the accuracy of the system in a population of 12 excised breast tissue specimens while establishing and implementing the scan room procedures across multiples specimens; and (3) Utilizing a concurrently-developed classification scheme to more thoroughly compare the system’s fidelity and robustness against pathology-assessed outcomes, which currently serve as the clinical gold standard for breast cancer judgments.

The typical workflow included Surgical Pathology preparing the surgically excised specimens and indicating via palpation the location of the tumor. The specimen, with the preliminary tumor location marked, was then scanned in our imaging system, and spectral scatter signatures were obtained at multiple locations within the tissue. The resulting form factor spectra were then compared with reference spectra to classify the tissue as cancerous or non-cancerous (healthy). The tissue classification mapping was compared against the indicated tumor area or against pathology-stained microslides for verification of tumor diagnosis.

Formalin-fixation was found inconsequential for tissue classification, with fresh-to-formalin-fixed spectra correlations of 0.9782 and 0.9881 over 10 spot scans each for healthy and cancer tissue, respectively. The spatial resolution of the system was found to be 1.5 mm in the lateral direction and 5 mm along the beam path. Our CACSSI system was able to distinguish between cancerous and healthy areas in the tissue slices in a consistent manner, and the system was, on average, 82.93% accurate for the initial classification scheme and 83.70% accurate using a more quantitative classification scheme. Furthermore, we were able to achieve these results in a clinically relevant timeframe on the order of 30 minutes, integrating into the pathology workflow with minimal interruption. Aggregating these results CACSSI will continue to be developed for use as a clinical imaging tool in breast cancer assessment and other diagnostic purposes.